Regensburg 2025 – wissenschaftliches Programm
Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe
DS: Fachverband Dünne Schichten
DS 2: Layer Deposition
DS 2.4: Vortrag
Montag, 17. März 2025, 10:15–10:30, H14
Enabling FAIR Data Practices in MBE Growth and Characterization — •Andrea Albino1, Hampus Näsström1, Sarthak Kapoor1, Altuğ Yildirim2, Oliver Bierwagen2, Martin Albrecht3, and Sebastian Brückner1,3 — 1Department of Physics, Humboldt-Universität zu Berlin, Berlin, Germany — 2Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Berlin, Germany — 3Leibniz-Institut für Kristallzüchtung, Berlin, Germany
Data-driven materials science is transforming materials design by moving beyond traditional trial-and-error methods. Molecular beam epitaxy (MBE) experiments highlight the challenge of navigating complex parameter spaces [1], often exceeding human cognitive limits, particularly when integrating diverse datasets. This complexity is compounded by the absence of standardized models for capturing detailed experimental workflows and instrument diversity. Addressing these issues requires metadata aligned with FAIR (Findable, Accessible, Interoperable, Reusable) principles [2].
Within the NOMAD ecosystem (nomad-lab.eu) [3], we digitize the data lifecycle for MBE growth, including in-situ and ex-situ characterization. Key tools, like Electronic Laboratory Notebooks (ELNs), systematically document growth procedures, enabling streamlined data management and AI-driven analytics to optimize MBE processes.
[1] O. Bierwagen et al., J. Phys. Condens. Matter 28, 22 (2016) [2] M. Wilkinson et al., Sci. Data 3, 160018 (2016) [3] M. Scheidgen et al., J. Open Source Software 8, 5388 (2023)
Keywords: FAIR; Data Management; MBE; workflow; ELN